## pval_cutoff: 0.05
## lfc_cutoff: 1
## low_counts_cutoff: 10
General statistics
# Number of samples
length(counts_data)
## [1] 6
# Number of genes
nrow(counts_data)
## [1] 55487
# Total counts
colSums(counts_data)
## SRR13535276 SRR13535278 SRR13535280 SRR13535288 SRR13535290 SRR13535292
## 3107284 2321609 3701956 7929174 6330905 3686532
Create DDS objects
# Create DESeqDataSet object
dds <- get_DESeqDataSet_obj(counts_data, ~ treatment)
## [1] TRUE
## [1] TRUE
## [1] "DESeqDataSet object of length 55487 with 0 metadata columns"
## [1] "DESeqDataSet object of length 15188 with 0 metadata columns"
colData(dds)
## DataFrame with 6 rows and 25 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating .. SRP303354
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating .. SRP303354
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating .. SRP303354
## SRR13535288 RNA-Seq 300 12863728500 PRJNA694971 SAMN17587373 5128876770 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943372 C GSM5043450 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043450 C2C12 proliferating .. SRP303354
## SRR13535290 RNA-Seq 300 12849825300 PRJNA694971 SAMN17587371 5136077921 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943374 C GSM5043454 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043454 C2C12 proliferating .. SRP303354
## SRR13535292 RNA-Seq 300 10569142200 PRJNA694971 SAMN17587369 4229018065 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943376 C GSM5043457 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043457 C2C12 proliferating .. SRP303354
Sample-to-sample comparisons
# Transform data (blinded rlog)
rld <- get_transformed_data(dds)
PCA plot
pca <- rld$pca
pca_df <- cbind(as.data.frame(colData(dds)) %>% rownames_to_column(var = 'name'), pca$x)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 30.6369 29.5690 26.7750 23.5645 20.988 4.616e-14
## Proportion of Variance 0.2662 0.2480 0.2033 0.1575 0.125 0.000e+00
## Cumulative Proportion 0.2662 0.5142 0.7176 0.8750 1.000 1.000e+00
ggplot(pca_df, aes(x = PC1, y = PC2, color = label)) +
geom_point() +
geom_text(aes(label = name), position = position_nudge(y = -2), show.legend = F, size = 3) +
scale_color_manual(values = colors_default) +
scale_x_continuous(expand = c(0.2, 0))
Correlation heatmap
pheatmap(
cor(rld$matrix),
annotation_col = as.data.frame(colData(dds)) %>% select(label),
color = brewer.pal(8, 'YlOrRd')
)
Wald test results
# DE analysis using Wald test
dds_full <- DESeq(dds)
colData(dds_full)
## DataFrame with 6 rows and 26 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study sizeFactor
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character> <numeric>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating .. SRP303354 0.679518
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating .. SRP303354 0.949141
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating .. SRP303354 0.775037
## SRR13535288 RNA-Seq 300 12863728500 PRJNA694971 SAMN17587373 5128876770 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943372 C GSM5043450 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043450 C2C12 proliferating .. SRP303354 1.903462
## SRR13535290 RNA-Seq 300 12849825300 PRJNA694971 SAMN17587371 5136077921 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943374 C GSM5043454 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043454 C2C12 proliferating .. SRP303354 1.293435
## SRR13535292 RNA-Seq 300 10569142200 PRJNA694971 SAMN17587369 4229018065 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.. SRX9943376 C GSM5043457 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043457 C2C12 proliferating .. SRP303354 0.820641
# Wald test results
res <- results(
dds_full,
contrast = c('treatment', condition, control),
alpha = pval_cutoff
)
res
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 15188 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46468 1.192223 0.980393 1.216066 0.223960 NA
## ENSMUSG00000103922 2.19055 -0.264400 2.004893 -0.131877 0.895081 NA
## ENSMUSG00000033845 170.96155 -0.410660 0.542059 -0.757592 0.448695 0.897547
## ENSMUSG00000102275 2.42483 0.445773 1.599930 0.278620 0.780536 NA
## ENSMUSG00000025903 145.03192 -0.144159 0.305941 -0.471199 0.637499 0.941449
## ... ... ... ... ... ... ...
## ENSMUSG00000061654 89.83965 -5.639498 3.758749 -1.500366 NA NA
## ENSMUSG00000079834 57.89103 -0.191890 0.531060 -0.361333 0.71785066 0.959205
## ENSMUSG00000095041 282.40751 -0.142151 0.680850 -0.208784 0.83461669 0.980566
## ENSMUSG00000063897 38.58802 0.249799 0.436318 0.572515 0.56697292 0.928150
## ENSMUSG00000095742 8.84404 2.668715 0.872880 3.057370 0.00223289 0.138082
mcols(res)
## DataFrame with 6 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized c..
## log2FoldChange results log2 fold change (ML..
## lfcSE results standard error: trea..
## stat results Wald statistic: trea..
## pvalue results Wald test p-value: t..
## padj results BH adjusted p-values
summary(res)
##
## out of 15188 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 9, 0.059%
## LFC < 0 (down) : 72, 0.47%
## outliers [1] : 179, 1.2%
## low counts [2] : 3527, 23%
## (mean count < 8)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
plotDispEsts(dds_full)
Summary details
# Upregulated genes (LFC > 0)
res_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res[which(is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 179 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000103509 9.61465 -4.46237 2.56030 -1.74291 NA NA
## ENSMUSG00000079554 38.15136 -6.55179 2.04362 -3.20597 NA NA
## ENSMUSG00000085842 32.45798 3.92441 2.26209 1.73486 NA NA
## ENSMUSG00000103553 13.01159 -4.72236 2.90747 -1.62422 NA NA
## ENSMUSG00000102425 25.59836 6.42741 2.37852 2.70227 NA NA
## ... ... ... ... ... ... ...
## ENSMUSG00000024867 25.1588 -1.33806 1.33535 -1.00203 NA NA
## ENSMUSG00000117704 65.4156 -4.86080 1.98458 -2.44928 NA NA
## ENSMUSG00000025089 56.0190 -1.68541 1.37462 -1.22610 NA NA
## ENSMUSG00000048029 24.8196 -6.69369 3.90828 -1.71269 NA NA
## ENSMUSG00000061654 89.8396 -5.63950 3.75875 -1.50037 NA NA
# Low counts (only padj is NA)
res[which(is.na(res$padj) & !is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 3527 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46468 1.192223 0.980393 1.216066 0.223960 NA
## ENSMUSG00000103922 2.19055 -0.264400 2.004893 -0.131877 0.895081 NA
## ENSMUSG00000102275 2.42483 0.445773 1.599930 0.278620 0.780536 NA
## ENSMUSG00000103280 3.07360 -0.631912 1.204891 -0.524456 0.599961 NA
## ENSMUSG00000033740 2.71686 -2.681434 2.566083 -1.044952 0.296045 NA
## ... ... ... ... ... ... ...
## ENSMUSG00000064342 5.31912 -0.3225041 1.294467 -0.2491404 0.803252 NA
## ENSMUSG00000064344 5.55025 -0.7333533 1.329458 -0.5516184 0.581210 NA
## ENSMUSG00000064349 4.21295 -0.0813581 1.110029 -0.0732937 0.941572 NA
## ENSMUSG00000064358 2.53260 1.6658901 1.341770 1.2415618 0.214398 NA
## ENSMUSG00000064369 6.67428 0.8693932 0.980304 0.8868608 0.375154 NA
Shrunken LFC results
plotMA(res)
# Shrunken LFC results
res_shrunken <- lfcShrink(
dds_full,
coef = str_c('treatment_', condition, '_vs_', control),
type = 'apeglm'
)
res_shrunken
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 15188 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46468 0.04484650 0.196771 0.223960 NA
## ENSMUSG00000103922 2.19055 -0.00240059 0.191823 0.895081 NA
## ENSMUSG00000033845 170.96155 -0.04683613 0.188929 0.448695 0.897547
## ENSMUSG00000102275 2.42483 0.00639198 0.191482 0.780536 NA
## ENSMUSG00000025903 145.03192 -0.04144011 0.167089 0.637499 0.941449
## ... ... ... ... ... ...
## ENSMUSG00000061654 89.83965 -0.00740276 0.192885 NA NA
## ENSMUSG00000079834 57.89103 -0.02214728 0.182797 0.71785066 0.959205
## ENSMUSG00000095041 282.40751 -0.01024471 0.185774 0.83461669 0.980566
## ENSMUSG00000063897 38.58802 0.04167765 0.181520 0.56697292 0.928150
## ENSMUSG00000095742 8.84404 1.89965145 1.103225 0.00223289 0.138082
plotMA(res_shrunken)
mcols(res_shrunken)
## DataFrame with 5 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized c..
## log2FoldChange results log2 fold change (MA..
## lfcSE results posterior SD: treatm..
## pvalue results Wald test p-value: t..
## padj results BH adjusted p-values
summary(res_shrunken, alpha = pval_cutoff)
##
## out of 15188 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 9, 0.059%
## LFC < 0 (down) : 72, 0.47%
## outliers [1] : 179, 1.2%
## low counts [2] : 3527, 23%
## (mean count < 8)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
Summary details
# Upregulated genes (LFC > 0)
res_shrunken_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_shrunken_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res_shrunken[which(is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 179 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000103509 9.61465 -0.0173337 0.193723 NA NA
## ENSMUSG00000079554 38.15136 -0.0340687 0.197165 NA NA
## ENSMUSG00000085842 32.45798 0.0191760 0.193938 NA NA
## ENSMUSG00000103553 13.01159 -0.0129806 0.193266 NA NA
## ENSMUSG00000102425 25.59836 0.0226261 0.194639 NA NA
## ... ... ... ... ... ...
## ENSMUSG00000024867 25.1588 -0.02592654 0.193589 NA NA
## ENSMUSG00000117704 65.4156 -0.02680544 0.195361 NA NA
## ENSMUSG00000025089 56.0190 -0.02975094 0.194687 NA NA
## ENSMUSG00000048029 24.8196 -0.00752951 0.192901 NA NA
## ENSMUSG00000061654 89.8396 -0.00740276 0.192885 NA NA
# Low counts (only padj is NA)
res_shrunken[which(is.na(res_shrunken$padj) & !is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 3527 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46468 0.04484650 0.196771 0.223960 NA
## ENSMUSG00000103922 2.19055 -0.00240059 0.191823 0.895081 NA
## ENSMUSG00000102275 2.42483 0.00639198 0.191482 0.780536 NA
## ENSMUSG00000103280 3.07360 -0.01599164 0.191196 0.599961 NA
## ENSMUSG00000033740 2.71686 -0.01341707 0.193044 0.296045 NA
## ... ... ... ... ... ...
## ENSMUSG00000064342 5.31912 -0.00681405 0.190794 0.803252 NA
## ENSMUSG00000064344 5.55025 -0.01463269 0.191602 0.581210 NA
## ENSMUSG00000064349 4.21295 -0.00230978 0.189871 0.941572 NA
## ENSMUSG00000064358 2.53260 0.03393515 0.195227 0.214398 NA
## ENSMUSG00000064369 6.67428 0.03247049 0.193121 0.375154 NA
Visualizing results
Heatmaps
# Plot normalized counts (z-scores)
pheatmap(counts_sig_norm[2:7],
color = brewer.pal(8, 'YlOrRd'),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
scale = 'row',
fontsize_row = 10,
height = 20)
# Plot log-transformed counts
pheatmap(counts_sig_log[2:7],
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
fontsize_row = 10,
height = 20)
# Plot log-transformed counts (top 24 DE genes)
pheatmap(counts_sig_log %>% filter(ensembl_gene_id %in% (res_sig_df %>% head(24))$ensembl_gene_id) %>% select(-ensembl_gene_id) %>% column_to_rownames(var = 'mgi_symbol'),
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = T,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
fontsize = 10,
fontsize_row = 10,
height = 20)
Volcano plots
# Unshrunken LFC
res_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
# Shrunken LFC
res_shrunken_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
GSEA (all)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
GSEA (DE)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
System info
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /home/chan/mRNA_seq_pipeline/.snakemake/conda/9a19315a020c824d12f8055f7c009b0f/lib/libopenblasp-r0.3.18.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] fgsea_1.20.0 RColorBrewer_1.1-2 pheatmap_1.0.12 DESeq2_1.34.0 SummarizedExperiment_1.24.0 Biobase_2.54.0 MatrixGenerics_1.6.0 matrixStats_0.61.0 GenomicRanges_1.46.0 GenomeInfoDb_1.30.0 IRanges_2.28.0 S4Vectors_0.32.0 BiocGenerics_0.40.0 scales_1.1.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
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## loaded via a namespace (and not attached):
## [1] colorspace_2.0-2 ellipsis_0.3.2 XVector_0.34.0 fs_1.5.1 rstudioapi_0.13 farver_2.1.0 bit64_4.0.5 mvtnorm_1.1-3 AnnotationDbi_1.56.1 fansi_0.4.2 apeglm_1.16.0 lubridate_1.8.0 xml2_1.3.3 splines_4.1.0 cachem_1.0.6 geneplotter_1.72.0 knitr_1.35 jsonlite_1.7.2 broom_0.7.10 annotate_1.72.0 dbplyr_2.1.1 png_0.1-7 compiler_4.1.0 httr_1.4.2 backports_1.4.0 assertthat_0.2.1 Matrix_1.3-4 fastmap_1.1.0 cli_3.1.0 htmltools_0.5.2 tools_4.1.0 coda_0.19-4 gtable_0.3.0 glue_1.5.1 GenomeInfoDbData_1.2.7 fastmatch_1.1-3 Rcpp_1.0.7 bbmle_1.0.24 cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.3.8 Biostrings_2.62.0 xfun_0.28 rvest_1.0.2 lifecycle_1.0.1 XML_3.99-0.8 MASS_7.3-54 zlibbioc_1.40.0 vroom_1.5.7 hms_1.1.1 parallel_4.1.0 yaml_2.2.1 memoise_2.0.1 gridExtra_2.3 emdbook_1.3.12 bdsmatrix_1.3-4 stringi_1.7.6 RSQLite_2.2.8 highr_0.9 genefilter_1.76.0 BiocParallel_1.28.0 rlang_0.4.12 pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14 lattice_0.20-45 labeling_0.4.2 bit_4.0.4 tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 R6_2.5.1 generics_0.1.1 DelayedArray_0.20.0 DBI_1.1.1 pillar_1.6.4 haven_2.4.3 withr_2.4.3 survival_3.2-13 KEGGREST_1.34.0 RCurl_1.98-1.5 modelr_0.1.8 crayon_1.4.2 utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11 locfit_1.5-9.4 grid_4.1.0 readxl_1.3.1 data.table_1.14.2 blob_1.2.2 reprex_2.0.1 digest_0.6.29 xtable_1.8-4 numDeriv_2016.8-1.1 munsell_0.5.0